Assessing the Impacts of Adaptation to Native‐Range Habitats and Contemporary Founder Effects on Genetic Diversity in an Invasive Fish
Bibliographic record
Abstract
ABSTRACT Species invading non‐native habitats can cause irreversible environmental damage and economic harm. Yet, how introduced species become widespread invaders remains poorly understood. Adaptation within native‐range habitats and rapid adaptation to new environments may both influence invasion success. Here, we examine these hypotheses using 7058 SNPs from 36 native, 40 introduced and 19 farmed populations of tench, a fish native to Eurasia. We examined genetic structure among these populations and accounted for long‐term evolutionary history within the native range to assess whether introduced populations exhibited lower genetic diversity than native populations. Subsequent to infer genotype–environment correlations within native‐range habitats, we assessed whether adaptation to native environments may have shaped the success of some introduced populations. At the broad scale, two glacial refugia contributed to the ancestry and genomic diversity of tench. However, native, introduced and farmed populations of admixed origin exhibited up to 10‐fold more genetic diversity (i.e., observed heterozygosity, expected heterozygosity and allelic richness) compared to populations with predominantly single‐source ancestry. The effects of introduction to a new location were also apparent as introduced populations exhibited fewer private alleles (mean = 9.9 and 18.9 private alleles in introduced and native populations, respectively) and higher population‐specific Fst compared to native populations, highlighting their distinctiveness relative to the pool of allelic frequencies across tench populations. Finally, introduced populations with varying levels of genetic variation and similar genetic compositions have become established and persisted under strikingly different climatic and ecological conditions. Our results suggest that lack of prior adaptation and low genetic variation may not consistently hinder the success of introduced populations for species with a demonstrated ability to expand their native range.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".